vector_embedding_dimension_reducer_pca

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions preserving >90% variance, which is a behavioral guarantee, and includes the cost (0.035 USDC on Base L2), which is useful operational context. However, it does not describe the return format, error behavior, or whether the operation is read-only, though that is implied by the nature of the task. It adds some value but is not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that front-loads the core purpose. It includes the cost information, which is a useful extra detail but not strictly part of the core functionality. The structure is efficient with no redundant phrasing, earning a high score for conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (PCA with parameters for dimensions and input format) and the lack of an output schema, the description is incomplete. It does not specify the payload structure, how to set the desired output dimension (2D/3D), or the expected input vector format. An agent would need additional guidance to invoke this tool correctly. The description also does not clarify the return value shape, which is critical for downstream use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100% for the two parameters, but the description adds minimal meaning about them. It does not explain the structure of the payload (e.g., how to specify the input vectors or the target dimension). It only mentions the output dimensionality (2D/3D) in the description, which is not directly tied to a parameter. Since the schema already documents the parameters generically, the description adds little extra semantic value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: Principal Component Analysis dimension reduction for compressing high-dimensional vectors to 2D/3D while preserving variance. It specifies the verb (compressing/reducing), the resource (high-dimensional vectors), and the goal (visualization). This distinguishes it from sibling tools that handle embeddings or vector operations but focus on different tasks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for visualization but does not explicitly state when to use this tool over alternatives or any exclusions. It does not mention any conditions under which this tool should not be used, nor does it reference sibling tools. The context 'for visualization' gives a general hint but lacks clear guidance on selecting this tool versus others.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.